Attenuated total reflectance-Fourier transform infrared spectroscopy (FTIR-ATR) combined with pattern recognition is applied to establish a rapid discrimination model of olive oil. Measurements of FTIR-ATR spectra of pure olive oil samples and pure soybean oil samples from different brands and mixed samples of pure olive oil doped with soybean oil are performed. The obtained spectra between 625 cm-1 and 4000 cm-1 are regarded as the research object of discrimination analysis. The principal component analysis (PCA) is applied to the dimension reduction of these spectra. Identification of the pure olive oil samples, pure soybean oil samples and three samples of olive oil doped with soybean oil is carried out by two pattern recognition methods, the Fisher discrimination and multilayer perception neural network. The experiment results show that the identification effect after the wavelet denoising is the best among all the adopted pretreatment methods. The original discriminant accuracy of discrimination model established by PCA combined with Fisher discriminant analysis reaches 100%. The cross validation accuracy reaches 97.1%. Classification accuracy of the training set and the test set by multilayer perception neural network identification model is 100% . Therefore, FTIR-ATR spectroscopy combined with pattern recognition method can realize the identification of the pure olive oil samples, pure soybean oil samples and three different oil blend samples. This method has some advantages over other methods owing to its easy operation, high speed and reliability.